Life = Content

Writing

Best Free AI Models for Hermes Agent (August 2026)

updated 2026-08-12

What this page is not. Other roundups rank stealth models such as Owl Alpha, or auto-routers such as openrouter/free. Those lists change weekly. This page is a field guide to six models that showed up in a Hermes Agent selector in August 2026, plus the honest "free vs paid" terms. Verify live listings before you build a workflow on any :free id.

Hermes Agent lets you run AI coding agents on your own hardware or through cloud APIs. The desktop app is free. Many of the models you can connect to are also free — at least for now.

This guide covers six free models that appear in the Hermes Agent model selector as of August 2026: Solar Pro 4, Tencent HY3, Poolside Laguna S 2.1, StepFun Step 3.7 Flash, Poolside Laguna XS 2.1, and Sakana Fugu Ultra. For each model, you get the specs, the benchmarks, the weaknesses, and a direct comparison against the paid frontier models you would otherwise use.

If you are searching for "free AI models for Hermes Agent", "best free LLMs 2026", or "Solar Pro 4 free Hermes", this page is built to answer those queries directly. For install, hooks, memory, and profiles, see How to Set Up Hermes Agent.

Quick Comparison: All Six Free Models at a Glance

ModelProviderTotal ParamsActive ParamsContextOutputBenchmark HighlightBest ForFree Until
Solar Pro 4UpstageNot disclosedNot disclosed524K128KAgentic work focus; 90% off launchMulti-step agent tasks, document work90% off through Sep 10; free on Nous Portal for 1 week
Tencent HY3Tencent295B (MoE)21B262KNot disclosedAgent workflows, reasoningAgent loops, rough drafts, simple codingFree tier live on OpenRouter as of Aug 12, 2026; verify current status
Laguna S 2.1Poolside118B (MoE)8B262K32K70.2% Terminal-Bench 2.1; 40.4% DeepSWEAgentic coding, long-horizon terminal workOngoing free tier (inputs/outputs may be used for training)
Step 3.7 FlashStepFun196B (MoE + vision)~11B256KNot disclosed59.5 Terminal-Bench 2.1; 56.3 SWE-Bench Pro; 76.5 SWE-Bench Verified; multimodalCoding + vision, agentic workflows, local deploymentOngoing (open weights available)
Laguna XS 2.1Poolside33B (MoE)3B256K32K70.9% SWE-Bench Verified; 37.5% Terminal-Bench 2.0Lightweight local coding agent, fast iterationOngoing free tier (inputs/outputs may be used for training)
Fugu UltraSakana AIOrchestrator (not a single model)N/A1MN/AClaims parity with Fable 5 and Mythos on some benchmarks (self-measured)Hard multi-step problems where you want a committee of modelsOngoing (orchestration overhead is the real cost)

Reading the table: "Active params" is how many parameters the model actually uses per token. A 118B model with 8B active costs about the same to run as an 8B model. This is the Mixture-of-Experts (MoE) trick that makes some large models cheap to inference.

1. Solar Pro 4 (Upstage AI)

What it is. Solar Pro 4 is Upstage's flagship agentic model, released August 11, 2026. It is built for multi-step work: reading documents, running tools, producing deliverables, and stopping when the evidence runs out instead of hallucinating.

Specs.

  • Context window: 524K tokens (OpenRouter lists this as 524K; Upstage's own materials say 512K — confirmed as the same window with different rounding)
  • Maximum output: 128K tokens
  • Languages: English, Korean, Japanese (input and output)
  • Reasoning effort: configurable (high for deep analysis, low for real-time chat speed)
  • API: OpenAI-compatible. Change the endpoint and model name (solar-pro4) to migrate.
  • License: Proprietary. Available through Upstage Console, OpenRouter, SolarChat, and Hermes Agent.

The "free" situation. Solar Pro 4 is not free. Two things are happening at the same time, and the model selector in Hermes labels it "Free" because of the Nous Portal piece:

  1. Nous Portal free week. Solar Pro 4 is free on Nous Research's portal for one week from launch (August 11–18, 2026). This is the "Free" label you see in the dropdown.
  2. 90% off on OpenRouter and Upstage Console through September 10. The regular price on OpenRouter is $0.1837 per 1M input tokens and $0.899 per 1M output tokens (these are the effective prices after the 90%-off promo shows at $0.03/$0.12). At 90% off, the promo price drops to roughly $0.03 per 1M input and $0.12 per 1M output. That is cheap, but it is not free.

Benchmarks. Upstage published agent-focused evaluations showing "several-fold" improvement over Solar Pro 3 on long documents, terminal tasks, and multi-turn tool use. Scores are from Artificial Analysis as of August 2026; Upstage says public listing of their results is upcoming. The model was trained on OfficeVerse, Upstage's pipeline that synthesizes office tasks from real public data across 11 industry domains and 12 task types, graded pass/fail on the final deliverable.

Strengths.

  • Built for finishing multi-step jobs, not just answering questions
  • 512K context lets you load several contracts or data files into one session
  • Reasoning effort is a dial — high for analysis, low for speed
  • Strong multilingual support (English, Korean, Japanese)
  • OpenAI-compatible API makes it easy to drop into existing agent setups

Weaknesses.

  • Not actually free — the "Free" label is a one-week portal promotion or a 90% discount
  • Parameter count and independent benchmark scores are not publicly available yet
  • Proprietary model — you cannot run it locally
  • The 90% off ends September 10, after which the full price applies

How it stacks up against paid frontier models. Solar Pro 4 is positioned as an agentic workhorse, not a frontier general-intelligence model. It is not trying to beat GPT-5.5 or Claude Opus 4.8 on Humanity's Last Exam. It is trying to finish contracts, reconcile numbers, and run terminal tasks without dropping steps. Against paid models in the same agentic-coding lane, it is early — the public benchmark data is thin. At 90% off, the price per task is competitive with budget-tier paid models, but not with free options.

Best use in Hermes. Multi-step agent tasks where you need the model to carry a job to the end. Document review, data reconciliation, terminal-based workflows. Use it during the free week or the 90%-off window to test whether it fits your workflow before the full price hits.

Hermes setup. Point Hermes at the OpenRouter endpoint with model upstage/solar-pro4 (during the 90%-off window) or use the Nous Portal integration. On OpenRouter, the model ID is upstage/solar-pro4.

2. Tencent HY3 (free tier)

What it is. HY3 is Tencent's Mixture-of-Experts model from the Hunyuan (混元) family. It is built for reasoning, agentic workflows, and production use. The free tier on OpenRouter gives you access to a 295B-parameter MoE model at no listed token cost.

Specs.

  • Total parameters: 295B (Mixture-of-Experts)
  • Active parameters: 21B per token (192 experts, top-8 routing)
  • Context window: 262K tokens (Tencent states 256K; OpenRouter lists 262K — same window: 256 × 1024 = 262,144)
  • Reasoning: configurable — no-think mode (default), low chain-of-thought, high chain-of-thought
  • Features: function calling, tool choice, structured outputs, reasoning tokens
  • API: OpenAI-compatible via OpenRouter (tencent/hy3:free)

The "free" situation. The free tier on OpenRouter was released July 6, 2026 and is still listed as :free as of August 12, 2026. Early reports around launch suggested the free API window might be time-limited, but the OpenRouter listing has remained active for over a month. Verify the current status before building a workflow that depends on it. The model weights are open-source on Hugging Face (tencent/Hy3), so you can also run HY3 locally if you have the hardware — the most air-gapped option if you need to keep data off Tencent's servers.

The fine print on OpenRouter: if you use HY3 for free through OpenRouter, your inputs and outputs may be used to train and improve their models — the same disclaimer OpenRouter applies to other free-tier models. Do not send proprietary code or confidential data through the OpenRouter free tier. If you need a no-training guarantee, run the open weights locally or use a paid tier.

Independent benchmark data for the free variant is thin. The DesignForOnline review (assessed August 9, 2026) ranks HY3 free at #153 of 688 overall, #41 of 193 for coding, and #47 of 175 for agentic tasks. The paid HY3 listing on OpenRouter has a coding index of 58.8 on Kilo Code.

Benchmarks.

  • HY3 free: no independent benchmark results published for the free variant specifically
  • HY3 (paid): coding index 58.8 on Kilo Code
  • Tencent claims HY3 preview tops benchmarks for overall usability and agent capabilities
  • Reddit reports (r/hermesagent, r/AISEOInsider) describe HY3 running 495-step agent workflows for free

Strengths.

  • Large MoE model (295B total, 21B active) — the active parameter count is what matters for inference cost
  • Configurable reasoning levels let you trade speed for depth
  • 262K context is enough for most codebase-scale tasks
  • OpenAI-compatible, works with Hermes through OpenRouter
  • Strong on agent workflows and multi-step planning according to community reports

Weaknesses.

  • Free tier may have a time limit — verify current status before building a workflow that depends on it
  • No independent benchmark data for the free variant specifically
  • Your inputs and outputs may be used for training on the OpenRouter free tier
  • Ranking (#153 overall) is well behind the frontier paid models
  • Inference speed is moderate (OpenRouter measures 63 tok/s P50 across providers; one reviewer measured ~64.2 tok/s)

How it stacks up against paid frontier models. HY3 free is a budget agent model, not a frontier model. On coding, it ranks in the top quartile of all tracked models (#41 of 193), which is respectable for a free tier. Against Claude Opus 4.8 (88.6% SWE-Bench Verified) or GPT-5.5, the gap is large. The right comparison is against other free models — HY3 sits in the middle of the free-tier pack on coding, with stronger agentic workflow support than some smaller models.

Best use in Hermes. Agent workflows, rough drafts, simple coding tasks, research summarization. The configurable reasoning levels make it useful for tasks where you want the model to think before answering. Do not use it for proprietary code unless you confirm the training policy or move to a paid tier.

Hermes setup. Set your model to tencent/hy3:free via OpenRouter. Use /model tencent/hy3:free in the Hermes CLI. Verify the free tier is still active before relying on it.

3. Poolside Laguna S 2.1 (free tier)

What it is. Laguna S 2.1 is Poolside's latest coding agent model, released July 21, 2026. It is a Mixture-of-Experts model designed specifically for software engineering and agentic coding. Poolside calls it "the most capable agentic coding model in its weight class by a wide margin."

Specs.

  • Total parameters: 118B (Mixture-of-Experts)
  • Active parameters: 8B per token
  • Context window: 262K tokens (Poolside and OpenRouter materials state 262K; this equals 256 × 1024 = 262,144)
  • Maximum output: 32K tokens
  • Features: function calling, tool choice, structured outputs, reasoning tokens
  • License: OpenMDW-1.1 (open weights available on Hugging Face at poolside/Laguna-S-2.1)
  • API: OpenAI-compatible via OpenRouter (poolside/laguna-s-2.1:free), also available through Kilo Code, Hermes Agent, pi, OpenCode, OpenClaw, Cline, and Poolside's own terminal agent

The "free" situation. The free tier on OpenRouter is ongoing. There is no time limit. The catch: if you use Laguna S 2.1 for free, Poolside may use your inputs and outputs to train and improve their models. This is disclosed on the OpenRouter model page. Same warning as HY3 — no proprietary code on the free tier.

The paid version of Laguna S 2.1 costs $0.10 per 1M input and $0.20 per 1M output, with a 1,049K context window. The free tier gives you 262K context at $0.

Benchmarks.

  • Terminal-Bench 2.1: 70.2% — Poolside claims this makes it the top model in its weight class, ahead of models 10x its size
  • DeepSWE: 40.4%
  • On the Terminal-Bench compiled leaderboard, it placed 11th overall, ahead of DeepSeek-V4-Pro-Max (64.0%, 1.6T parameters), Thinking Machines Inkling (63.8%, 975B parameters), and NVIDIA Nemotron 3 Ultra (56.4%, 550B parameters)

Strengths.

  • Outperforms much larger models on Terminal-Bench 2.1 — the MoE architecture (118B total, 8B active) delivers frontier-tier coding at a fraction of the inference cost
  • Open weights under OpenMDW-1.1 — you can download and run it locally if you have the hardware
  • 262K context handles large codebases
  • Strong on long-horizon terminal tasks, which is what agentic coding actually is
  • Available across multiple platforms (OpenRouter, Kilo, Hermes, pi, OpenCode, OpenClaw, Cline)

Weaknesses.

  • Free tier may use your inputs/outputs for training
  • 32K max output is limiting for very long agent runs
  • 262K context on the free tier vs. 1,049K on the paid tier
  • Agentic coding focus means it may be less strong on non-coding tasks (writing, analysis, general reasoning)
  • OpenMDW-1.1 license has usage restrictions — read the license before commercial deployment

How it stacks up against paid frontier models. On Terminal-Bench 2.1, Laguna S 2.1 (70.2%) trails GPT-5.5 (78.2%) and Claude Opus 4.8 (74.6%). The gap is real but not enormous given the price difference — free vs. $0.10/$0.20 per 1M on the paid Laguna tier, and vs. $1.25/$10 on GPT-5.5 or $5/$25 on Opus 4.8. For pure agentic coding in a terminal loop, Laguna S 2.1 is the strongest free option available. For full issue resolution across module boundaries, Claude Opus 4.8 is still the benchmark to beat.

Best use in Hermes. Agentic coding tasks, terminal-based development workflows, long-horizon software engineering tasks where you want a model that can pursue a multi-step plan without losing track. The open weights also make it the best free model to self-host if you have the GPU capacity.

Hermes setup. Set your model to poolside/laguna-s-2.1:free via OpenRouter. Use /model poolside/laguna-s-2.1:free in the Hermes CLI. If you want to run it locally, pull from Hugging Face (poolside/Laguna-S-2.1) and serve via vLLM or llama.cpp.

4. StepFun Step 3.7 Flash (free tier)

What it is. Step 3.7 Flash is StepFun's high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters per token. It is one of the few free models that can see images and videos, not just text.

Specs.

  • Total parameters: 196B language backbone (MoE) + vision encoder
  • Active parameters: ~11B per token (quoted as 11B in StepFun materials)
  • Context window: 256K tokens (GitHub and StepFun blog confirm 256K; OpenRouter lists 262K — same window with different rounding)
  • Multimodal: yes — native image and video understanding
  • Reasoning: selectable levels (high/medium/low)
  • Quantization: NVFP4 (available on Hugging Face as stepfun-ai/Step-3.7-Flash-NVFP4)
  • Local deployment: supported via vLLM, llama.cpp, and other runtimes
  • API: OpenAI-compatible via StepFun's API; available through OpenRouter; accessible from Hermes Agent

The "free" situation. Step 3.7 Flash is available through StepFun's own API (no :free designation on their platform — it is a paid API with a trial possibility) and through OpenRouter (check current listing for :free availability, as the status may change). The model weights are open on Hugging Face, which means you can run it entirely locally — the most genuinely free option on this list if you have the hardware. Local deployment requires a GPU with enough VRAM for the NVFP4 quantized weights.

Community reports on Reddit (r/hermesagent, r/LocalLLaMA) describe strong local performance. One reviewer deployed it on an NVIDIA DGX Spark within 24 hours and reported 100% tool call success rate.

Benchmarks.

  • Terminal-Bench 2.1: 59.5%
  • SWE-Bench Pro: 56.3% — beats DeepSeek V4 Flash (55.6%) and Gemini 3.5 Flash (55.1%)
  • SWE-Bench Verified: 76.5%
  • Toolathlon: 49.5%
  • SimpleVQA (Search): 79.2 (first place)
  • V* (Python): 95.3 (frontier parity)
  • ClawEval: 67.1 (first place among flash-tier models tested)
  • GDPVal-AA: 45.8%

Strengths.

  • Multimodal — the only model on this list that handles images and video natively
  • Open weights on Hugging Face — genuinely free to self-host
  • Strong on both coding (SWE-Bench Pro 56.3, Verified 76.5) and general agent tasks (Terminal-Bench 2.1 59.5)
  • Selectable reasoning levels let you trade speed for depth
  • Beats other flash-tier models (DeepSeek V4 Flash, Gemini 3.5 Flash) on SWE-Bench Pro and ClawEval
  • Local deployment is viable on consumer hardware with the NVFP4 quantization

Weaknesses.

  • SWE-Bench Pro (56.3) still trails Claude Opus 4.8 (64.3) and GPT-5.5 (58.6) — the gap to the frontier is clear
  • Terminal-Bench 2.1 (59.5) trails Laguna S 2.1 (70.2%) for pure coding agent tasks
  • Parameter count (196B) means local deployment needs a serious GPU — not a laptop model
  • Max output token limit is not prominently disclosed on OpenRouter; the StepFun materials do not state a hard cap — verify before long agent runs
  • The OpenRouter free-tier availability may have rate limits or be time-limited — check current status; StepFun's own API is a paid endpoint

How it stacks up against paid frontier models. Step 3.7 Flash is a strong flash-tier model. It beats other flash-tier models (DeepSeek V4 Flash, Gemini 3.5 Flash) on coding benchmarks. Against frontier paid models, it trails — Claude Opus 4.8 scores 64.3 on SWE-Bench Pro vs. 56.3 for Step 3.7 Flash, and 88.6 on SWE-Bench Verified vs. 76.5. The gap is about 8–12 points on coding. But Step 3.7 Flash is free and multimodal, which neither Opus 4.8 nor GPT-5.5 is. For a developer who needs image understanding in an agent workflow at zero cost, Step 3.7 Flash is the only real option.

Best use in Hermes. Multimodal agent workflows (reading charts, UI screenshots, diagrams), coding tasks where you want open weights you can self-host, and situations where the model's vision capability adds value beyond what a text-only model can do. If you have the GPU for local deployment, this is the most cost-effective multimodal agent model available for free.

Hermes setup. Check OpenRouter for the current stepfun/step-3.7-flash listing and :free availability. For local deployment, pull stepfun-ai/Step-3.7-Flash-NVFP4 from Hugging Face and serve via vLLM. The StepFun API endpoint is https://api.stepfun.ai/v1.

5. Poolside Laguna XS 2.1 (free tier)

What it is. Laguna XS 2.1 is Poolside's lightweight agentic coding model, the little brother to Laguna S 2.1. It is a 33B total parameter Mixture-of-Experts model with only 3B activated per token, designed for agentic coding and long-horizon work on local hardware. It is the model to run when you want Poolside's coding quality at a footprint small enough for a laptop or desktop GPU.

Specs.

  • Total parameters: 33B (Mixture-of-Experts)
  • Active parameters: 3B per token
  • Context window: 256K tokens (OpenRouter lists 262K for the free tier — same window with OpenRouter's standard rounding; Poolside and Ollama materials state 256K)
  • Maximum output: 32K tokens
  • Quantization: FP8 for fast, cost-efficient inference
  • Features: function calling, tool choice, structured outputs, reasoning tokens, interleaved thinking between tool calls
  • Architecture: mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio across 40 total layers, sigmoid gating with per-layer rotary scales
  • License: OpenMDW-1.1 (open weights on Hugging Face at poolside/Laguna-XS-2.1)
  • Local runtime: available via Ollama (ollama run laguna-xs-2.1), vLLM, and other runtimes
  • Hardware: runs at 160+ tok/s on Apple M5 Max (reported)

The "free" situation. Same as Laguna S 2.1 — the free tier on OpenRouter is ongoing. The same training-data catch applies: if you use Laguna XS 2.1 for free, Poolside may use your inputs and outputs to train and improve their models. The paid tier costs $0.10 per 1M input and $0.20 per 1M output with a 262K context window. The free tier gives you 256K context at $0.

The model is available on Ollama, which means you can run it entirely locally with no API costs after the one-time download. This makes it the most genuinely free option for local development on consumer hardware.

Benchmarks.

  • SWE-Bench Verified: 70.9%
  • SWE-Bench Multilingual: 63.1%
  • SWE-Bench Pro (public dataset): 47.6%
  • Terminal-Bench 2.0: 37.5%

Strengths.

  • Small footprint (33B total, 3B active) — runs on consumer hardware, including Apple Silicon
  • Fast inference — 160+ tok/s on Apple M5 Max
  • Open weights under OpenMDW-1.1 — download and run locally
  • Available on Ollama for one-command local deployment
  • Strong on SWE-Bench Verified (70.9%) for its size class — beats many larger models
  • 256K context handles large codebases locally
  • Interleaved thinking between tool calls supports agentic workflows

Weaknesses.

  • Terminal-Bench 2.0 (37.5%) is significantly behind Laguna S 2.1 (70.2%) — the smaller model sacrifices long-horizon terminal performance
  • SWE-Bench Pro (47.6%) is well behind the frontier paid models
  • Free tier may use your inputs/outputs for training
  • 32K max output limits very long agent runs
  • OpenMDW-1.1 license has usage restrictions
  • The smaller active parameter count (3B) means it may struggle with tasks that need deeper reasoning than coding

How it stacks up against paid frontier models. Laguna XS 2.1 is a size-class model, not a frontier model. Its 70.9% on SWE-Bench Verified is impressive for a 33B model, but Claude Opus 4.8 scores 88.6% and GPT-5.5 is the frontier standard. The right comparison is against other small local models — Laguna XS 2.1 beats most of them on coding. Against free models, it is the fastest and most locally deployable option. The trade-off is clear: you give up long-horizon terminal performance (Terminal-Bench 2.0 at 37.5%) to gain speed and local deployability.

Best use in Hermes. Local development on consumer hardware, fast iteration cycles, coding tasks where you want agentic quality without sending data to a cloud API. The Ollama deployment path makes it the easiest free model to run entirely on your own machine. Use it for coding tasks that fit within its reasoning capacity; escalate to Laguna S 2.1 or a cloud model when the task gets too deep.

Hermes setup. Set your model to poolside/laguna-xs-2.1:free via OpenRouter. For local deployment, run ollama run laguna-xs-2.1 and point Hermes at your local Ollama endpoint (http://localhost:11434/v1). The model ID in Ollama is laguna-xs-2.1.

6. Sakana Fugu Ultra

What it is. Fugu Ultra is not a single model. It is a learned multi-agent orchestration system — a language model trained to route tasks across a pool of underlying frontier models and to recursively call instances of itself. Sakana AI, a Japanese research lab, built it on the thesis that a well-coordinated committee of models can match or beat a single frontier model.

Specs.

  • Type: multi-agent orchestration system, not a monolithic model
  • Context window: 1M tokens
  • Features: configurable reasoning effort, tool calling, built-in web search, function calling
  • Modalities: text input/output, vision/image input supported
  • Pool: routes across a swappable pool of frontier models (confirmed to include Google Gemini; self-reported to also route to Claude and GPT-class models)
  • Architecture: orchestrator model trained to select, delegate, verify, and synthesize across worker agents; Fugu Ultra uses deeper multi-agent orchestration than the base Fugu model
  • Research: two papers accepted at ICLR 2026

The "free" situation. Fugu Ultra is available on OpenRouter. The pricing is not simply "free" — the orchestration model itself may have a cost, and the underlying model calls it makes add up. One hands-on review clocked a light question at 108 seconds with about 60% of the billed tokens spent on back-channel orchestration the user never sees. Heavy Fugu Ultra tasks can run to roughly $10 per message. This is not a free model in the same sense as Laguna XS 2.1 or Step 3.7 Flash. The "Free" label in the Hermes dropdown likely reflects the OpenRouter free-tier availability of the orchestrator endpoint, but the orchestration overhead means a single task can cost real money.

Benchmarks. Sakana's self-reported benchmarks claim Fugu Ultra "stands shoulder-to-shoulder" with frontier models like Fable 5 and Mythos Preview — neither of which is in its routing pool — and outscores available big models (Opus 4.8, Gemini 3.1 Pro, GPT 5.5) on several benchmarks. The fine print:

  • The headline scorecard is self-measured
  • Compared against vendor-reported baselines
  • Shows Fugu losing one row in Sakana's own table
  • Hides demo competitors behind shuffled labels
  • Ignores the token/latency cost of orchestration

A detailed independent review (Medium, Towards AI) concluded: "Believe the direction. Don't yet believe the destination, not on this evidence." Sakana was caught gaming a benchmark in 2025, which adds skepticism.

The base Fugu model (not Ultra) beats Fugu Ultra on some benchmarks in Sakana's own table — SciCode (60.1 vs 58.7) and τ³ Banking (21.7 vs 20.6) — which is an odd thing to publish next to a "use Ultra for hard problems" pitch.

Strengths.

  • 1M context window — the largest on this list
  • Orchestration means no single provider outage or price hike takes you down
  • Built-in web search and tool calling
  • The orchestration approach is a genuine research direction — multi-agent coordination can beat single models, and the ICLR 2026 papers back that up
  • Useful for hard, high-stakes problems where you want a committee of models checking each other

Weaknesses.

  • Not free in any meaningful sense — orchestration overhead can run to $10 per message
  • Self-measured benchmarks with methodological concerns; the independent review is skeptical
  • High latency — a light question clocked at 108 seconds
  • 60% of billed tokens can be orchestration overhead the user never sees
  • Fixed-pool routing is proprietary — you cannot swap out the underlying models
  • Sakana's 2025 benchmark-gaming history adds credibility risk
  • The base Fugu beats Fugu Ultra on some of Sakana's own benchmarks

How it stacks up against paid frontier models. Fugu Ultra claims parity with frontier models, but the evidence is self-measured and methodologically questionable. There is no independent benchmark confirmation that Fugu Ultra matches Claude Opus 4.8 or GPT-5.5 on hard tasks. Even if the claim holds, the cost per task is higher than calling a single frontier model directly, because of orchestration overhead. The real value of Fugu is resilience (no single provider dependency) and the research direction, not a clear cost or quality advantage over a single frontier model.

Best use in Hermes. Hard multi-step problems where you want multiple models checking each other's work, and where you are willing to pay for the orchestration overhead. Not a general-purpose free model — treat it as a specialized tool for specific high-stakes tasks. If you want a free model for daily coding work, pick Laguna S 2.1 or Step 3.7 Flash instead. If you want the frontier, pick Claude Opus 4.8 or GPT-5.5 and pay the indicated price — you will get clearer benchmarks and lower per-task cost.

Hermes setup. Check OpenRouter for the current sakana/fugu-ultra listing and pricing. Be aware that orchestration tasks can accumulate cost quickly — monitor your OpenRouter usage when testing.

How These Free Models Stack Up Against Paid Frontier Models

The paid frontier models as of August 2026 are Claude Opus 4.8 (Anthropic), GPT-5.5 (OpenAI), and Gemini 3.1 Pro (Google). Here is how the free models compare.

On coding (SWE-Bench and Terminal-Bench)

ModelSWE-Bench VerifiedSWE-Bench ProTerminal-BenchCost
Claude Opus 4.888.6%64.3%74.6% (TB 2.0)$5/$25 per 1M
GPT-5.5—58.6%78.2% (TB 2.1)~$1.25/$10 per 1M
Laguna S 2.1 (free)—40.4% (DeepSWE)70.2% (TB 2.1)$0
Laguna XS 2.1 (free)70.9%47.6%37.5% (TB 2.0)$0
Step 3.7 Flash (free)76.5%56.3%59.5% (TB 2.1)$0
Tencent HY3 (free)No SWE/Terminal scores publishedNo SWE/Terminal scores publishedNo SWE/Terminal scores published$0 (free tier live)

Reading this: Step 3.7 Flash is the strongest free model on SWE-Bench Verified (76.5%) and SWE-Bench Pro (56.3%). Laguna S 2.1 is the strongest on Terminal-Bench (70.2%). Both trail the frontier paid models but at zero cost. Laguna XS 2.1 is strong for its size (70.9% SWE-Bench Verified) but weak on long-horizon terminal work. HY3 does not publish SWE-Bench or Terminal-Bench scores; its ranking (#41 of 193 for coding on DesignForOnline) comes from a different benchmark methodology.

On reasoning (Humanity's Last Exam)

ModelHLE (no tools)HLE (with tools)Cost
Claude Opus 4.849.8%57.9%$5/$25 per 1M
GPT-5.541.4%52.2%~$1.25/$10 per 1M
Free modelsNot benchmarkedNot benchmarked$0

Reading this: The free models on this list do not publish Humanity's Last Exam scores. This is the frontier reasoning benchmark, and the gap between free and paid is largest here. If your work needs frontier-level reasoning, free models are not yet competitive.

On context window

ModelContextCost
Fugu Ultra1MOrchestration cost
Claude Opus 4.81M$5/$25 per 1M
Gemini 3.1 Pro1M+Paid
Solar Pro 4524K90% off through Sep 10
Tencent HY3262K (OpenRouter) / 256K (Tencent)$0 (free tier)
Laguna S 2.1262K$0 (free tier)
Step 3.7 Flash256K$0 (open weights)
Laguna XS 2.1256K$0 (free tier)

Reading this: Fugu Ultra and Claude Opus 4.8 offer 1M context. Solar Pro 4 offers 524K (OpenRouter) / 512K (Upstage). The rest of the free models top out at 256K–262K, which is enough for most codebase-scale tasks but not for whole-repository or book-length contexts.

The honest summary

Free models are viable for coding agent tasks, especially agentic terminal work. The strongest free options (Laguna S 2.1, Step 3.7 Flash) score within striking distance of paid flash-tier models on coding benchmarks, at zero cost. They are not competitive with frontier paid models on reasoning (Humanity's Last Exam) or on full issue resolution across module boundaries (SWE-Bench Pro, where Opus 4.8 leads at 64.3% vs. 56.3% for the best free model).

The right question is not "are free models as good as paid?" It is "what is the cost of being wrong?" If a coding error means a quick fix, free models are the rational default. If a coding error means a production outage, pay for the frontier model.

Which Free Model Should You Pick?

Match the model to the job.

For agentic coding in a terminal loop: Laguna S 2.1 (free). It scores 70.2% on Terminal-Bench 2.1, the best on this list for that specific workload.

For coding with vision (charts, screenshots, diagrams): Step 3.7 Flash. The only multimodal free model on the list. Strong on both coding (SWE-Bench Pro 56.3) and vision (SimpleVQA 79.2, V* 95.3).

For local development on consumer hardware: Laguna XS 2.1. Runs on Ollama with one command. Fast on Apple Silicon. The 33B/3B footprint fits on a laptop GPU.

For multi-step agent work with documents: Solar Pro 4 during the free week or 90%-off window. Built for finishing jobs, not just answering questions. 512K context. Test it while it is cheap — the full price is not free.

For agent workflows and rough drafts: Tencent HY3 (free tier on OpenRouter as of August 12, 2026). Configurable reasoning levels, 262K context, strong community reports on multi-step agent tasks. The model weights are also open-source on Hugging Face (tencent/Hy3), so you can run it locally for a no-training guarantee. Verify the OpenRouter free tier is still active before relying on it.

For hard problems where you want a committee of models: Fugu Ultra. But understand the cost — orchestration overhead can run to $10 per message. Not a free model in practice.

For proprietary code or confidential data: Do not use any free tier that says it may use your inputs and outputs for training (HY3, Laguna S 2.1, Laguna XS 2.1). Use a paid tier, or run an open-weights model locally (Step 3.7 Flash, Laguna XS 2.1 via Ollama) where your data never leaves your machine.

FAQ: Free AI Models for Hermes Agent

What are the best free AI models for Hermes Agent in 2026?

The best free models for Hermes Agent in August 2026 are Poolside Laguna S 2.1 for agentic coding (70.2% Terminal-Bench 2.1), StepFun Step 3.7 Flash for coding plus vision (76.5% SWE-Bench Verified, multimodal), and Poolside Laguna XS 2.1 for local development on consumer hardware (70.9% SWE-Bench Verified, runs on Ollama). Solar Pro 4 is available free for one week on Nous Portal and at 90% off on OpenRouter through September 10. Tencent HY3 is available on a free tier through OpenRouter, but verify current availability. Sakana Fugu Ultra is an orchestration system, not a free model in the traditional sense — orchestration overhead can cost $10 per message.

Is Solar Pro 4 actually free?

No. Solar Pro 4 is free for one week (August 11–18, 2026) on Nous Research's portal, and it is 90% off on OpenRouter and Upstage Console through September 10, 2026. The "Free" label in the Hermes model selector reflects the Nous Portal promotion. The regular price is $0.1837 per 1M input and $0.899 per 1M output. At 90% off, that drops to roughly $0.03 per 1M input and $0.12 per 1M output. After September 10, the full price applies.

Can I use free AI models with Hermes Agent?

Yes. Hermes Agent supports free models through multiple providers. OpenRouter lists free models with a :free suffix. Rate limits apply, and they change — check OpenRouter's current free-tier page before you depend on a number. NVIDIA NIM offers free credits on signup with no credit card required. Hugging Face offers monthly free credits. Local models via Ollama are free after the one-time download — no API keys, no token billing, no usage caps. You switch models in Hermes with the /model command or hermes model.

Which free model is best for coding in Hermes Agent?

For cloud-based agentic coding, Poolside Laguna S 2.1 (free) is the strongest on Terminal-Bench 2.1 (70.2%). For coding with vision, StepFun Step 3.7 Flash is the best free multimodal option (76.5% SWE-Bench Verified, 56.3% SWE-Bench Pro). For local coding on consumer hardware, Poolside Laguna XS 2.1 runs on Ollama and scores 70.9% on SWE-Bench Verified at a 33B/3B footprint. Step 3.7 Flash is also available as open weights for local deployment if you have the GPU.

What is the difference between Laguna S 2.1 and Laguna XS 2.1?

Laguna S 2.1 is Poolside's larger model: 118B total parameters with 8B active, scoring 70.2% on Terminal-Bench 2.1 and 40.4% on DeepSWE. It is built for long-horizon agentic coding. Laguna XS 2.1 is the lightweight version: 33B total with 3B active, scoring 70.9% on SWE-Bench Verified but only 37.5% on Terminal-Bench 2.0. It is built for fast, local coding workflows. The S model is better for deep terminal work; the XS model is better for speed and local deployability. Both are available as open weights under the OpenMDW-1.1 license.

Does Step 3.7 Flash support images and video?

Yes. Step 3.7 Flash is multimodal — it has a vision encoder for native image and video understanding. It scores 79.2 on SimpleVQA (Search), first place, and 95.3 on V* (Python), frontier parity. It can process dense visual interfaces like UI wireframes, application GUIs, and data charts. This makes it the only free model on this list that can see images and video as part of an agent workflow.

Can I run free AI models locally with Hermes Agent?

Yes. Hermes Agent treats a local Ollama server as a custom endpoint — base URL http://localhost:11434/v1, any placeholder API key, and whatever model you have pulled. Free models you can run locally include Poolside Laguna XS 2.1 (Ollama: ollama run laguna-xs-2.1), StepFun Step 3.7 Flash (open weights on Hugging Face, deployable via vLLM or llama.cpp), and Poolside Laguna S 2.1 (open weights on Hugging Face at poolside/Laguna-S-2.1). Local deployment requires sufficient GPU VRAM — Laguna XS 2.1 runs on consumer hardware including Apple Silicon, while Laguna S 2.1 and Step 3.7 Flash need more capacity.

Are free AI model tiers safe for proprietary code?

Generally no, if the provider's terms say they may use your inputs and outputs for training. Tencent HY3, Poolside Laguna S 2.1, and Poolside Laguna XS 2.1 all disclose on OpenRouter that free-tier inputs and outputs may be used to train and improve their models. Do not send proprietary code, customer data, or confidential information through these free tiers. For proprietary work, use a paid tier with a clear no-training policy, or run an open-weights model locally (Step 3.7 Flash, Laguna XS 2.1 via Ollama) where your data never leaves your machine.

What is Fugu Ultra and is it free?

Fugu Ultra is a multi-agent orchestration system from Sakana AI, not a single model. It routes tasks across a pool of frontier models and recursively calls instances of itself. It is available on OpenRouter, but it is not free in the traditional sense — orchestration overhead can run to roughly $10 per message for heavy tasks, and a light question clocked at 108 seconds with about 60% of billed tokens spent on orchestration the user never sees. Sakana claims Fugu Ultra matches frontier models like Fable 5 and Mythos, but the benchmarks are self-measured and an independent review advises treating the direction as real and the destination as unproven.

How does Solar Pro 4 compare to Claude Opus 4.8 and GPT-5.5?

Solar Pro 4 is not directly comparable to Claude Opus 4.8 or GPT-5.5 on general intelligence benchmarks — Upstage has not published Humanity's Last Exam or similar scores. Solar Pro 4 is positioned as an agentic workhorse trained on OfficeVerse (office tasks across 11 industry domains, graded pass/fail on final deliverables). Its strengths are multi-step agent tasks, long documents (524K context on OpenRouter / 512K per Upstage), and tool use. Claude Opus 4.8 leads on SWE-Bench Pro (64.3% vs. unpublished for Solar Pro 4) and Humanity's Last Exam (49.8%). GPT-5.5 leads on Terminal-Bench 2.1 (78.2% vs. unpublished for Solar Pro 4). Solar Pro 4's advantage is its focus on finishing real work and its 90%-off price through September 10, which makes it competitive with budget-tier paid models for agentic tasks during the promotion window.

Should I use openrouter/free or a named :free model?

Use a named model id when you need a repeatable setup. openrouter/free picks a random free model that supports the features you asked for. That is fine for a throwaway prompt. It is a poor default for an agent loop, because the model can change between turns. This page names six models from a Hermes selector snapshot. Verify each id is still listed before you depend on it.

Sources

← back to writing